Observability
The Swiss AI Hub provides visibility into agent execution processes. Users can see the steps an agent executed, the decisions it made, and the data it processed.
Why observability matters
Traditional AI systems show results without showing reasoning. Users see responses but can't see intermediate steps, decision logic, or where things went wrong.
Decision-makers hesitate to rely on systems they can't understand or validate. When AI produces unexpected results, users need information to determine whether the system malfunctioned or correctly processed flawed input.
Development teams debugging unexpected agent behavior need more than log files. Quality assurance teams need systematic ways to validate agent behavior.
Regulated industries need to explain and justify AI-assisted decisions. Compliance frameworks require evidence showing how systems reached conclusions and where human oversight occurred.
How execution tracing works
The platform captures detailed records of agent workflow execution.
Execution traces show the complete sequence of steps the agent executed - decision points, tool invocations, knowledge retrievals, intermediate calculations. Users see structured processes rather than mysterious computations.
Each workflow step consumes input events and produces output events. The trace display shows how data transforms through the workflow. Input messages become classification events, then retrieval requests, then synthesized responses.
Traces include timing information for each step. Users can identify performance bottlenecks and assess whether slow responses come from complex reasoning or infrastructure delays.
When workflows include conditional logic, traces show which branches executed and why. This helps users validate that agents apply appropriate logic to specific scenarios.
Interactive exploration
Trace display opens an adjacent panel within the chat interface. Users correlate chat responses with workflow steps without switching applications.
Traces present information hierarchically - workflow overview, step execution, event data. Users drill into areas of interest without excessive detail for straightforward operations.
At the most granular level, users examine complete event data - the JSON structures flowing between workflow steps. This supports sophisticated debugging and validation.
From trace views, users navigate to related capabilities - knowledge documents accessed during retrieval, agent configurations, system logs.
Langfuse tracing integration
The platform uses Langfuse, an open-source AI observability platform.
The implementation follows OpenInference semantic conventions. Trace data uses standardized formats compatible with industry observability tools.
The system captures semantic events - LLM invocations, retrieval operations, embedding generations - as structured trace spans. These capture AI-specific concepts like token usage, retrieval relevance scores, and model selection.
When workflows involve multiple agents, traces maintain correlation across interactions. Users can follow execution flows spanning multiple agents.
Execution traces persist beyond conversation sessions. Users can review historical executions for quality assurance, compliance documentation, or incident investigation.
What this provides
When agents behave unexpectedly, execution traces enable rapid troubleshooting. Support teams examine execution sequences, identify failure points, and resolve issues without extensive reproduction.
Quality assurance teams validate agent behavior systematically. By examining how agents process inputs and handle edge cases, QA verifies correctness before production deployment.
Developers identify optimization opportunities from trace data. Traces revealing inefficient patterns or unnecessary steps guide refinement efforts.
For regulatory compliance, execution traces document how systems reached conclusions. Audits can review traces demonstrating appropriate data usage and human oversight at required points.
When users can examine execution details, confidence increases. The ability to inspect processes makes AI more understandable.
Developer use
Developers building agents use trace visualization during development. Rather than log files or print statements, developers watch workflows execute through trace interfaces.
Automated tests capture execution traces. Tests can verify that agents invoked appropriate tools, accessed correct knowledge sources, and followed expected paths.
Trace timing data enables performance profiling. Developers identify slow steps, quantify configuration impacts, and validate optimization results.
Execution traces serve as living documentation. Developers reference actual traces showing how agents behave in practice.
Privacy and security
Trace visibility respects the permission system. Users can only view traces for conversations they participated in or are authorized to audit. Administrative access requires explicit permissions.
The platform can redact sensitive information from traces - personally identifiable information, confidential data - while preserving workflow structure.
The system logs trace access, documenting who reviewed which traces and when. This supports compliance requirements and detects inappropriate access.
Organizations configure trace retention policies balancing observability value against storage costs and regulations. Traces can expire after periods, or selective retention can preserve significant conversations while aging out routine interactions.